Fake social media profile detection using machine learning
Anurag Shukla, Shreya Chaurasia, Tanushri Asthana, Tej Narayan Prajapati, Vivek Kushwaha · 2025
The pervasive growth of social media platforms has brought about a surge in the creation of fake profiles, posing significant challenges to online security, trust, and information integrity. With the exponential rise of social media platforms, the prevalence of fake profiles has become a critical concern, leading to various malicious activities and misinformation dissemination. The study encompasses a diverse range of approaches, including machine learning algorithms, natural language processing, behavioral analysis, and network-based methods. The review begins by outlining the motivations behind the creation of fake profiles, emphasizing the potential harm they inflict on individuals, organizations, and the broader online community. Subsequently, it delves into the various features and characteristics commonly exploited by researchers to distinguish between authentic and deceptive profiles. These features encompass textual, visual, and behavioral attributes, revealing the multidimensional nature of fake profile detection. This study focuses on the development and implementation of a robust fake social media profile detection system. Leveraging machine learning techniques, specifically Convolution Neural Networks (CNN), the model is trained on a dataset comprising genuine and fake social media profiles. Features including user names, gender classification, language usage, and profile statistics are extracted and utilized to discern patterns indicative of fake profiles. The model’s performance is evaluated through rigorous testing and validation, showcasing its efficacy in accurately identifying fake profiles. The proposed detection system contributes to the ongoing efforts to mitigate the adverse impacts of fake profiles on social media platforms, promoting a safer and more trustworthy online environment.